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Table 19: is set for movies based on final priority for recommendation International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.5, October 2012

Table 19 is set for movies based on final priority for recommendation International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.5, October 2012

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We propose the architecture of SNetRS as shown in the following Figure 1: Architecture of SNetRS.
We propose the architecture of SNetRS as shown in the following Figure 1: Architecture of SNetRS.
Table 0: Contains data of two wheeler and movies with their short forms which we are going to use to explain the example We have taken into consideration the location and gender as parameters for the two wheeler vehicles(Table 2) and language and age group as parameters for the movies(Table 1).
Table 0: Contains data of two wheeler and movies with their short forms which we are going to use to explain the example We have taken into consideration the location and gender as parameters for the two wheeler vehicles(Table 2) and language and age group as parameters for the movies(Table 1).
Table 1: Survey of movies among different age group (Language is the key parameter for recommendation.)
Table 1: Survey of movies among different age group (Language is the key parameter for recommendation.)
Table 2: Survey of vehicles among people of different gender (Location is the key parameter for recommendation.) The aim is to find the likes of devang (Table 3 and 4) in the experimental products based on hi: rating given for the products in the training dataset (table 5 and 6).
Table 2: Survey of vehicles among people of different gender (Location is the key parameter for recommendation.) The aim is to find the likes of devang (Table 3 and 4) in the experimental products based on hi: rating given for the products in the training dataset (table 5 and 6).
Table 4: Likes of devang for movies, taken from Facebook. Table 3: Likes of devang for two wheelers, taken from Facebook.
Table 4: Likes of devang for movies, taken from Facebook. Table 3: Likes of devang for two wheelers, taken from Facebook.
Table 5: Training data for two wheeler.
Table 5: Training data for two wheeler.
Table 8 : Ratings(1 to 5) of movies taken by survey. Table 7 : Ratings(1 to 5) of two wheeler vehicle taken by survey. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.5, October 2012
Table 8 : Ratings(1 to 5) of movies taken by survey. Table 7 : Ratings(1 to 5) of two wheeler vehicle taken by survey. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.5, October 2012
Table 10 : satisfaction rate of each movie. Note: gray colored items are already liked by user. Table 9 : satisfaction rate of each two wheeler. Note: gray colored products are already liked by user
Table 10 : satisfaction rate of each movie. Note: gray colored items are already liked by user. Table 9 : satisfaction rate of each two wheeler. Note: gray colored products are already liked by user
Table 11: set of items liked by user “devang”. Step 2: We obtain the users with similar likes as that of user “devang” and Find number of common likes of other user for user “devang”’. See table 12
Table 11: set of items liked by user “devang”. Step 2: We obtain the users with similar likes as that of user “devang” and Find number of common likes of other user for user “devang”’. See table 12
Table 12 : users with similar likes as that of user “devang” Step 3: Find the other likes of the users set. Set the priority of user, based on from Table 13 and 14 ehavior count.
Table 12 : users with similar likes as that of user “devang” Step 3: Find the other likes of the users set. Set the priority of user, based on from Table 13 and 14 ehavior count.
Table 14 : other likes of the users set with priority for movies Table 13 : other likes of the users set with priority for two wheelers.
Table 14 : other likes of the users set with priority for movies Table 13 : other likes of the users set with priority for two wheelers.
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.5, October 2012 Step 5: Remove the items that are already liked by user “devang”’. Step 6: Find frequency of product that are common between users. See table 15 and 16
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.5, October 2012 Step 5: Remove the items that are already liked by user “devang”’. Step 6: Find frequency of product that are common between users. See table 15 and 16
Table 15: is the set for two wheeler vehicles with frequency count. Table 16: is the set for movies with frequency count.
Table 15: is the set for two wheeler vehicles with frequency count. Table 16: is the set for movies with frequency count.
Step 7: Find the final priority for recommendation, Summation of : Priority + Frequency + Ratings. See table 18 and 19
Step 7: Find the final priority for recommendation, Summation of : Priority + Frequency + Ratings. See table 18 and 19
Table 18: is set for two wheeler vehicle based on final priority for recommendation
Table 18: is set for two wheeler vehicle based on final priority for recommendation
Table 20: is the final recommendation for movies. HP is removed from the recommendation as its satisfaction rate is “zero” as per item base algorithm. Table 21: is the final recommendation for two wheeler vehicle.
Table 20: is the final recommendation for movies. HP is removed from the recommendation as its satisfaction rate is “zero” as per item base algorithm. Table 21: is the final recommendation for two wheeler vehicle.
Conclusion And Future Work We conclude from our research and analysis that, scope of recommendation is much in e- commerce domain. Recommendation using social networking information will really help in recommending the best product suitable to the user. Social networking is the best means of knowing user behavior. We are going to have further research on the same topic. We plan to implement this model and to add time factor and cross-domain filtering. Time factor model will help in knowing the rating gaps base on time. Cross — domain filtering will help to know the purpose of user, visiting our site. From cross-domain filtering system will get an idea, about the product user is looking for.
Conclusion And Future Work We conclude from our research and analysis that, scope of recommendation is much in e- commerce domain. Recommendation using social networking information will really help in recommending the best product suitable to the user. Social networking is the best means of knowing user behavior. We are going to have further research on the same topic. We plan to implement this model and to add time factor and cross-domain filtering. Time factor model will help in knowing the rating gaps base on time. Cross — domain filtering will help to know the purpose of user, visiting our site. From cross-domain filtering system will get an idea, about the product user is looking for.
Related topics:
Social NetworkingUser preferencesRecommendations systems (RS)
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